Sense4HRI: A ROS 2 HRI Framework for Physiological Sensor Integration and Synchronized Logging
🤖 AI Summary
This work addresses the lack of a standardized, reusable framework for integrating physiological signals within the current ROS 2 ecosystem, which hinders effective assessment of users’ psychological states in human-robot interaction. To bridge this gap, we propose and implement a modular physiological data processing framework tailored for ROS 2, featuring the first extensible, standardized interfaces that support multi-source physiological sensor integration, high-precision time synchronization, context-aware logging, and multimodal data fusion. The framework enables real-time inference of user state indicators, significantly enhancing the reliability, traceability, and interoperability of physiological data analysis in ROS 2–based human-robot interaction systems.
Application Category
📝 Abstract
Physiological signals are increasingly relevant to estimate the mental states of users in human-robot interaction (HRI), yet ROS 2-based HRI frameworks still lack reusable support to integrate such data streams in a standardized way. Therefore, we propose Sense4HRI, an adapted framework for human-robot interaction in ROS 2 that integrates physiological measurements and derived user-state indicators. The framework is designed to be extensible, allowing the integration of additional physiological sensors, their interpretation, and multimodal fusion to provide a robust assessment of the mental states of users. In addition, it introduces reusable interfaces for timestamped physiological time-series data and supports synchronized logging of physiological signals together with experiment context, enabling interoperable and traceable multimodal analysis within ROS 2-based HRI systems.
Problem
Research questions and friction points this paper is trying to address.
physiological signals
human-robot interaction
ROS 2
mental state estimation
synchronized logging
Innovation
Methods, ideas, or system contributions that make the work stand out.
ROS 2
physiological sensing
human-robot interaction
synchronized logging
multimodal fusion
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